Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.

Roadmap of environmental health research on emerging contaminants: Inspiration from the studies on engineered nanomaterials

Research on the environmental health of emerging contaminants is critical to understand their risks before causing severe harm. However, the low environmental concentrations, complex behaviors, and toxicology of emerging contaminants present enormous challenges for researchers. Here, we reviewed the research on the environmental health of engineered nanomaterials (ENMs), one of the typical emerging contaminants, to enlighten pathways for future research on emerging contaminants at their initial exploratory stage. To date, some developed pretreatment methods and detection technologies have been established for the determination of ENMs in natural environments. The mechanisms underlying the transfer and transformation of ENMs have been systematically explored in laboratory studies. The mechanisms of ENMs-induced toxicity have also been preliminarily clarified at genetic, cellular, individual, and short food chain levels, providing not only a theoretical basis for revealing the risk change and environmental health effects of ENMs in natural environments but also a methodological guidance for studying environmental health of other emerging contaminants. Nonetheless, due to the interaction of multiple environmental factors and the high diversity of organisms in natural environments, health effects observed in laboratory studies likely differ from those in natural environments. We propose a holistic approach and mesocosmic model ecosystems to systematically carry out environmental health research on emerging contaminants, obtaining data that determine the objectivity and accuracy of risk assessment.

Development of a Quasi-Quantitative Structure-Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials

The conventional Hill equation model is suitable to fit dose-response data obtained from performing (eco)toxicity assays. Models based on quasi-quantitative structure-activity relationships (QSARs) to estimate the Hill coefficient ( n H ) ${n}_{{\rm{H}}})$ were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi-simplified molecular input line entry system and correlated to experimentally derived values of n H ${n}_{{\rm{H}}}$ . Monte Carlo optimization was used to model the set of n H ${n}_{{\rm{H}}}$  values, and the model was trained on the basis of reported dose-response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose-response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose-response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose-response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;41:1439-1450. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Nano-QTTR development for interspecies aquatic toxicity of silver nanoparticles between daphnia and fish

Limited studies of quantitative toxicity-toxicity relationship (QTTR) modeling have been conducted to predict interspecies toxicity of engineered nanomaterials (ENMs) between aquatic test species. A meta-analysis of 66 publications providing acute toxicity data of silver nanoparticles (AgNPs) to daphnia and fish was performed, and the toxicity data, physicochemical properties, and experimental conditions were collected and curated. Based on Euclidean distance (ED) grouping, a meaningful correlation of logarithmic lethal concentrations between daphnia and fish was derived for bare (R²_bare = 0.47) and coated AgNPs (R²_coated = 0.48) when a distance of 10 was applied. The correlation of coated AgNPs was improved (R²_coated = 0.55) by the inclusion of descriptors of the coating materials. The correlations were further improved by R²_bare = 0.57 and R²_coated = 0.81 after additionally considering particle size only, and by R²_bare = 0.59 and R²_coated = 0.92 after considering particle size and zeta potential simultaneously. The developed ED-based nano-QTTR model demonstrated that inclusion of the coating material descriptors and physicochemical properties improved the goodness-of-fit to predict interspecies aquatic toxicity of AgNPs between daphnia and fish. This study provides insight for future in silico research on QTTR model development in ENM toxicology.

Cytotoxicity and genotoxicity of coated-gold nanoparticles on freshwater algae Pseudokirchneriella subcapitata

Gold engineered nanoparticles (nAu) are increasingly detected in ecosystems, and this raises the need to establish their potential effects on aquatic organisms. Herein, cytotoxic and genotoxic effects of branched polyethylenimine (BPEI)- and citrate (cit)-coated nAu (5, 20, and 40 nm) on algae Pseudokirchneriella subcapitata were evaluated. The apical biological endpoints: growth inhibition and chlorophyll a (Chl a) content were investigated at 62.5-1000 µg/L over 168 h. In addition, the apurinic/apyrimidinic (AP) sites, randomly amplified polymorphic deoxyribonucleic acid (RAPD) profiles, and genomic template stability (GTS) were assessed to determine the genotoxic effects of nAu. The results show algal growth inhibition at 5 nm BPEI-nAu up to 96 h, and thereafter cell recovery except at the highest concentration of 1000 µg/L. Insignificant growth reduction for cit-nAu (all sizes), as well as 20 and 40 nm BPEI-nAu, was observed over 96 h, but growth promotion was apparent at all exposures thereafter except for 40 nm BPEI-nAu at 250 µg/L. A decrease in Chl a content following exposure to 5 nm BPEI-nAu at 1000 µg/L corresponded to significant algal growth reduction. In genotoxicity studies, a significant increase in AP sites content was observed relative to the control - an indication of nAu ability to induce genotoxic effects irrespective of their size and coating type. For 5 nm- and 20 nm-sized nAu for both coating types and exposure concentrations no differences in AP sites content were observed after 72 and 168 h. However, a significant reduction in AP sites was observed following algae exposure to 40 nm-sized nAu (irrespective of coating type and exposure concentration) at 168 h compared to 72 h. Thus, AP sites results at 40 nm-size suggest likely DNA damage recovery over a longer exposure period. The findings on AP sites content showed a good correlation with an increase in genome template stability and growth promotion observed after 168 h. In addition, RAPD profiles demonstrated that nAu can induce DNA damage and/or DNA mutation to P. subcapitata as evidenced by the appearance and/or disappearance of normal bands compared to the controls. Therefore, genotoxicity results revealed significant toxicity of nAu to algae at the molecular level although no apparent effects were detectable at the morphological level. Overall, findings herein indicate that long-term exposure of P. subcapitata to low concentrations of nAu may cause undesirable sub-lethal ecological effects.

A meta-analysis framework to assess the role of units in describing nanoparticle toxicity

Despite ample research on nanoparticles, their environmental toxicity is still debatable. The lack of consensus is due in part to the challenge of comparing studies because of variability in parameters like test organism, test medium, and duration of experiment. However, the unit used to compare the toxicology of nanoparticles is one variable that experimentalists can control. Traditionally, mass per volume is the most common unit used to make comparisons, but there is growing evidence that alternative units such as surface area per volume or particles per volume may provide a better and more mechanistic measure of toxicity. Herein, we propose and test a meta-analytic framework to study the effect of units on nanotoxicology using data from the NanoE-Tox database, a freely available database containing 1518 toxicology values from 224 published articles of which 42 records met our basic inclusion criteria. These data were augmented with more recent data published over the past five years as archived by the Web of Science citation index. An additional 27 records from 1676 papers met the inclusion criteria and were also included in the analysis. The meta-analysis framework measures the degree of heterogeneity for each of three units (grams/L, particles/L, surface area/L) grouped by the type of test organism, particle chemistry, and manner in which a nanoparticle's size was measured (e.g., nominal particle size reported by the manufacturer vs. measurement of size for particles suspended in the liquid medium used in a subsequent toxicity experiment). The result of the meta-analysis reveals that surface area per volume reduces the heterogeneity in the Ag crustacean subgroup when nanoparticle size was measured in the test medium, and the ZnO crustacean subgroup when nanoparticle size was measured out the test medium and may therefore be a more appropriate estimate of the toxicity of soluble nanoparticles. No subgroups in our analysis showed a reduction in heterogeneity for particles per volume in either soluble or insoluble nanoparticles. The lack of conclusion on insoluble nanoparticles was not due to a limitation of our meta-analysis but rather highlights a critical deficiency in the primary literature. The majority of published studies fail to report common measures of error that are essential for further analysis (i.e. error of the measured nanoparticle size and/or interoperable error of the measured half-maximal concentration of the toxic endpoint). If future nanotoxicity studies report such error, as they should, then the framework of our meta-analysis could be used more broadly to provide a simple, statistically rigorous way to assess the role of units on the toxicity of nanoparticles.

Biosurfactant electrospun nanofibers exhibit minimal side effects on the structure and function of the liver tissue in male rat model

Oil spills can result in significant damage to marine estuaries, rivers, lakes, wetlands, and shorelines. Electrospun nanofibers containing biosurfactant (ENFs) can be used to clean oil spills up and protect the environmental biology. Present work aimed to study the side-effects of prepared nanofibers on animal models. Screening of the prepared ECNFs on animals showed that three of them (PVA-5, PEO-1, and PEO-5) are safe to hepatic tissues and liver functions. Furthermore, oxidative stress did not change after using these nanofibers. The PVA-1 nanofibers, however, were found to cause major pathological changes in the liver tissue. In addition, PVA-1 nanofibers were proved to alter the total white blood count and the neutrophil percentages significantly in comparison to the control. In conclusion, PVA-5, PEO-1, and PEO-5 are safe to hepatic tissues and liver functions.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

A Novel Experimental and Modelling Strategy for Nanoparticle Toxicity Testing Enabling the Use of Small Quantities

Metallic nanoparticles (NPs) differ from other metal forms with respect to their large surface to volume ratio and subsequent inherent reactivity. Each new modification to a nanoparticle alters the surface to volume ratio, fate and subsequently the toxicity of the particle. Newly-engineered NPs are commonly available only in low quantities whereas, in general, rather large amounts are needed for fate characterizations and effect studies. This challenge is especially relevant for those NPs that have low inherent toxicity combined with low bioavailability. Therefore, within our study, we developed new testing strategies that enable working with low quantities of NPs. The experimental testing method was tailor-made for NPs, whereas we also developed translational models based on different dose-metrics allowing to determine dose-response predictions for NPs. Both the experimental method and the predictive models were verified on the basis of experimental effect data collected using zebrafish embryos exposed to metallic NPs in a range of different chemical compositions and shapes. It was found that the variance in the effect data in the dose-response predictions was best explained by the minimal diameter of the NPs, whereas the data confirmed that the predictive model is widely applicable to soluble metallic NPs. The experimental and model approach developed in our study support the development of (eco)toxicity assays tailored to nano-specific features.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials

As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.

Developing species sensitivity distributions for metallic nanomaterials considering the characteristics of nanomaterials, experimental conditions, and different types of endpoints

A species sensitivity distribution (SSD) for engineered nanomaterials (ENMs) ranks the tested species according to their sensitivity to a certain ENM. An SSD may be used to estimate the maximum acceptable concentrations of ENMs for the purpose of environmental risk assessment. To construct SSDs for metal-based ENMs, more than 1800 laboratory derived toxicity records of metallic ENMs from >300 publications or open access scientific reports were retrieved. SSDs were developed for the metallic ENMs grouped by surface coating, size, shape, exposure duration, light exposure, and different toxicity endpoints. It was found that PVP- and sodium citrate- coatings enhance the toxicity of Ag ENMs as concluded from the relevant SSDs. For the Ag ENMs with different size ranges, differences in behavior and/or effect were only observed at high exposure concentrations. The SSDs of Ag ENMs separated by both shape and exposure duration were all nearly identical. Crustaceans were found to be the most vulnerable group to metallic ENMs. In spite of the uncertainties of the results caused by limited data quality and availability, the present study provided novel information about building SSDs for distinguished ENMs and contributes to the further development of SSDs for metal-based ENMs.

A model for screening and prioritizing consumer nanoproduct risks: A case study from South Africa

The potential risks of the increasing variety and volume of engineered nanomaterials (ENMs) entering into the ecosystem remain poorly quantified. In recent years, information essential to evaluate the ecological risks of ENMs has increased. However, the data are highly fragmented, limited, or severely lacking. This limits the usefulness of the information to support holistic screening and prioritization of potentially harmful ENMs. To screen and prioritize ENMs risks, we adopted a two-phased approach. First, a holistic framework model was developed to integrate a diverse set of factors aimed to assess the potential hazard, exposure, and in turn, risk to the ecosystem of ENMs from a given consumer nanoproduct. Secondly, using published literature we created a database of consumer nanoproduct categories, and types based on embedded ENMs type. The database consisted of eight consumer product categories, eleven different types of ENMs, and twenty-three nanoproduct types. The model results indicates the largest quantities of ENMs were released from sunscreens, textiles, cosmetics and paints with dominant ENMs quantities in descending order (based on quantity) as nTiO₂>nZnO>nSiO₂>nAg, and nFe₂O₃. In addition, according to the results from this study, nAg from washing machine were found to likely the highest risk to the environment. Overall, our model-derived results based on the case study illustrated: (i) the holistic framework's ability to screen, prioritize, rank, and compare ENMs potential exposure and risks among different nanoproducts categories and types, (ii) the derived risk estimations could support nanowastes classification with likelihood of non-uniformity of nanowastes classes even from the same nanoproduct category (e.g. cosmetics), and (iii) the lack of a mass-based criteria specific for EMNs impedes realistic exposure and risk evaluation in the ecological systems.

Skin Corrosion and Irritation Test of Nanoparticles Using Reconstructed Three-Dimensional Human Skin Model, EpiDermTM

Effects of nanoparticles (NPs) on skin corrosion and irritation using three-dimensional human skin models were investigated based on the test guidelines of Organization for Economic Co-operation and Development (OECD TG431 and TG439). EpiDerm^TM skin was incubated with NPs including those harboring iron (FeNPs), aluminum oxide (AlNPs), titanium oxide (TNPs), and silver (AgNPs) for a defined time according to the test guidelines. Cell viabilities of EpiDerm^TM skins were measured by the 3-(4, 5-dimethylthi-azol-2-yl)-2.5-diphenyltetrazolium bromide based method. FeNPs, AlNPs, TNPs, and AgNPs were non-corrosive because the viability was more than 50% after 3 min exposure and more than 15% after 60 min exposure, which are the non-corrosive criteria. All NPs were also non-irritants, based on viability exceeding 50% after 60 min exposure and 42 hr post-incubation. Release of interleukin 1-alpha and histopathological analysis supported the cell viability results. These findings suggest that FeNPs, AlNPs, TNPs, and AgNPs are ‘non-corrosive’ and ‘non-irritant’ to human skin by a globally harmonized classification system.

Toxicity of copper nanoparticles to Daphnia magna under different exposure conditions

Although the risks of metallic nanoparticles (NPs) to aquatic organisms have already been studied for >10years, our understanding of the link between the fate of particles in exposure medium and their toxicity is still in its infancy. Moreover, most of the earlier studies did not distinguish the contribution of particles and soluble ions to the toxic effects caused by suspensions of metallic NPs. In this study, the toxicity of CuNPs to Daphnia magna upon modification of the exposure conditions, achieved by aging the suspensions of CuNPs and by altering water chemistry parameters like the pH and levels of dissolved organic carbon (DOC), was investigated. The LC50 values for CuNPs exposure decreased by about 30% after 7days of aging. The LC50 values increased >12-fold upon addition of DOC at concentrations ranging from 0 to 10mg/L to the exposure medium. Changing the pH from 6.5 to 8.5 resulted in a 3-fold higher LC50 value. Furthermore, it was found that during 7days of aging of the exposure medium (without addition of DOC and at pH7.8), the toxicity could be mostly ascribed to the particles present in the suspension (around 70%). However, adding DOC or decreasing the pH of the exposure medium reduced the contribution of the particles to the observed toxicity. We thus found that the effective concentration regarding the toxicity was mainly driven by the contribution of the soluble ions in the presence of DOC or at pH6.5. Our results suggest that the toxicity results of CuNPs obtained from laboratory tests may overestimate the risk of the particles in polluted waters due to the common absence of DOC in laboratory test solutions. Moreover, the role of the ions shedding from CuNPs is very important in explaining the toxicity in natural waters.

Multilaboratory evaluation of 15 bioassays for (eco)toxicity screening and hazard ranking of engineered nanomaterials: FP7 project NANOVALID

Within EU FP7 project NANOVALID, the (eco)toxicity of 7 well-characterized engineered nanomaterials (NMs) was evaluated by 15 bioassays in 4 laboratories. The highest tested nominal concentration of NMs was 100 mg/l. The panel of the bioassays yielded the following toxicity order: Ag > ZnO > CuO > TiO₂>MWCNTs > SiO₂>Au. Ag, ZnO and CuO proved very toxic in the majority of assays, assumingly due to dissolution. The latter was supported by the parallel analysis of the toxicity of respective soluble metal salts. The most sensitive tests/species were Daphnia magna (towards Ag NMs, 24-h EC₅₀=0.003 mg Ag/l), algae Raphidocelis subcapitata (ZnO and CuO, 72-h EC₅₀=0.14 mg Zn/l and 0.7 mg Cu/l, respectively) and murine fibroblasts BALB/3T3 (CuO, 48-h EC₅₀=0.7 mg Cu/l). MWCNTs showed toxicity only towards rat alveolar macrophages (EC₅₀=15.3 mg/l) assumingly due to high aspect ratio and TiO₂ towards R. subcapitata (EC₅₀=6.8 mg Ti/l) due to agglomeration of TiO₂ and entrapment of algal cells. Finally, we constructed a decision tree to select the bioassays for hazard ranking of NMs. For NM testing, we recommend a multitrophic suite of 4 in vitro (eco)toxicity assays: 48-h D. magna immobilization (OECD202), 72-h R. subcapitata growth inhibition (OECD201), 30-min Vibrio fischeri bioluminescence inhibition (ISO2010) and 48-h murine fibroblast BALB/3T3 neutral red uptake in vitro (OECD129) representing crustaceans, algae, bacteria and mammalian cells, respectively. Notably, our results showed that these assays, standardized for toxicity evaluation of “regular” chemicals, proved efficient also for shortlisting of hazardous NMs. Additional assays are recommended for immunotoxicity evaluation of high aspect ratio NMs (such as MWCNTs).

Development of nanostructure–activity relationships assisting the nanomaterial hazard categorization for risk assessment and regulatory decision-making

Developed nano-SARs based on the state-of-art of ecotoxicity testing of metallic nanomaterials.